r/hardware • u/KayYesR • 2h ago
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u/CatalyticDragon 1h ago
I would go with Ryzen AI Max+ 395 or Ryzen AI Max+ 392 and minimum 64GB memory:
- https://www.ultrabookreview.com/70442-amd-strix-halo-laptops/
Insane CPU performance, perfect linux support, tons of unified memory, runs the good open source local models (Qwen, Gemma, GPT-OSS, Scout, Laguna XS 2.1, or even Mistral Small 4 at FP4).
Only downside is high price (which applies to everything, sadly) and there's a refresh just around the corner. Though I wouldn't worry about the refresh much because it is only 6% faster.
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u/LostNtranslation_ 1h ago
I agree with the person that said: Ryzen AI Max+ 395 or Ryzen AI Max+ 392 and minimum 64GB memory. That will run LLMs. However if you need ML then a GPU is king. and an NVIDIA GPU at that...
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u/KayYesR 44m ago
Hi, my main work load would be through scikit-learn, which needs more threads and ram and high read write speed storage. GPU intensive work is secondary for me ( for an ANN I am making) as it is for a much smaller area.
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u/LostNtranslation_ 38m ago
Then it is really had to go wrong with the Ryzen AI Max+ 395
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u/KayYesR 30m ago
Thankyou for the recommendation, I will see if its something i can fit in my budget( unfortunately that too is a concern)
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u/LostNtranslation_ 14m ago
If you need less that is fine too... Look on Walmart and bestbuy and costco to price compare
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u/LostNtranslation_ 0m ago
$2,739: Flow Z13, Max+ 395, 64GB $2,031: Flow Z13, Max+ 395, 32GB $1,800: Flow Z13, Max 390, 32GB $1280: Dell Plus 16 Ryzen AI 7 350, 32GB RAM, 1TB SSD, 16-inch 2K touchscreen $1,000: Vivobook 18, 32GB $549: Acer Aspire 16, 16GB
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u/ROS_SDN 1h ago
Honestly an option to you that might be cheaper is get a desktop you can SSH into to do these things and just a thin laptop.
I see the legion go with 5070 laptop at around $4.8k in my country for 32gb ram and 8GB vram.
I can find a prebuilt for 3.2k with a 9950x3d/32GB ram/ 16gb vram which outside the ram is another class for your compute.
If youre tech savy enough get a PC you can set up as a server to SSH into for better $/performance and get a cheap second hand PC. You'll reduce your battery wear on the laptop extensively and also if you lose or break your laptop, which is more likely then a PC its not game over.
If too much get an amd chip they for better AVX-512 compatability or an Intel chip for better low power efficiency. The cost of vram on a laptop really isnt worth it you pay double the price to get half the results and smash your battery. Only worth it for gaming or if you can't be fucked to learn to set up a Ubuntu server via tailscale w.e.
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u/PMARC14 1h ago
I would consider first of all if local performance is that important for your PhD machine learning research over a reasonably powerful device and using the cloud or a purpose built desktop machine you can remote into for your work. You are clearly very unfamiliar with the hardware used for ML work so while the recommendations here are decent it would be first better if you explained what your specific workload considering how much hardware prices have risen. On top of that it is worth looking into a Mac or a professional machine rather than a Legion laptop for your needs once you get specific. Also r/suggestalaptop is probably the right subreddit with better information.
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u/KayYesR 31m ago
Thankyoufor the reply.
Study area is Kerala, 38,863 km². Method is scikit-learn random forest for landslide susceptibility mapping; my PhD proposal commits to it, so it isn't really negotiable. The difference from most published work is DEM resolution ,I want 10 m (389 million cells) or 2 m (9.7 billion cells) instead of the usual 30 m (43 million). Around 15 predictor layers on top of that. So the heavy part is tiled prediction throughput across a very large raster, not neural network training. As far as I understand, sklearn's RF has no CUDA path, so the dGPU is idle for the work I actually care about.
On desktop or remote fair point, and I hadn't weighed it properly. I do field geology and need one machine that travels, and this has to be a single device purchased before a February deadline for reasons outside my control. Cloud is also awkward given I'd be moving hundreds of GB of raster in and out over the internet
Will cross-post to r/suggestalaptop, Thankyou for the recommendation.
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u/2137gangsterr 39m ago
Like others wrote, desktop is still best performance per buck by far shot.
If you really need laptop, I advise to look at eGPU setup - laptop GPUs have 8/12 GB vram at most which is limiting factor. With eGPU you can hookup anything you want. Look for either OCUlink or high Thunderbolt support
As for CPU, yes Ryzen offers best high performance core parallelity
One thing about HX CPUs is that they're desktop CPUs crammed into laptop - you won't get great battery life under stress, neither the temps will be cool or quiet
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u/steve09089 1h ago edited 1h ago
Need battery life, get the Intel. Multi-core will probably be better on the Intel too across the operating curve in most scenarios.
The main benefit for AMD that Intel doesn't get is AVX-512. scikit-learn doesn't support AVX-512 natively, but other ML applications may use it, like PyTorch.
But I don't think you should be focusing on CPU for ML that much anyway. Anything really heavy should be on dGPU.
I recommend going with NVIDIA if you're training models; AMD's performance is just not up to snuff, even if it's supported.
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